Why applied AI requires skills and knowledge beyond data science

Every year, machine learning researchers fascinate us with new discoveries and innovations. There are a dozen artificial intelligence conferences where researchers push the boundaries of science and show how neural networks and deep learning architectures can take on new challenges in areas such as computer vision and natural language processing.

But using machine learning in real-world applications and business problems—often referred to as “applied machine learning” or “applied AI”—presents challenges that are absent in academic and scientific research settings. Applied machine learning requires resources, skills, and knowledge that go beyond data science, that can integrate AI algorithms into applications used by thousands and millions of people every day.

Alyssa Simpson Rochwerger and Wilson Pang, two experienced practitioners of applied machine learning, discuss these challenges in their new book Real World AI: A Practical Guide for Responsible Machine learning. Rochwerger, a former director of product at IBM Watson, and Pang, the CTO of Appen, draw on their personal experience and knowledge to provide many examples of how organizations succeeded or failed in integrating machine learning into their products and business models.

Real World AI explains the common challenges and pitfalls of machine learning strategies and how product leaders can avoid repeating the failures of other organizations. Here are four of the key challenges that Rochwerger and Pang highlight in their book.

Knowing the problem you want to solve is a challenge that applies to all software engineering tasks. Any experienced developer will acknowledge that “doing the right thing” is different from “doing the thing right.” In applied machine learning, defining the problem plays a crucial role in the choices you make for the technologies, data sources, and people who will be working on your product.

“Only 20 percent of AI in pilot stages at major companies make it to production, and many fail to serve their customers as well as they could,” Rochwerger and Pang write in Real World AI. “In some cases, it’s because they’re trying to solve the wrong problem. In others, it’s because they fail to account for all the variables—or latent biases—that are crucial to a model’s success or failure.”

Consider image classification problems. Deep neural networks can perform such tasks with stunning accuracy. But if you want to apply them to a real application, a detailed definition of the problem will determine the kind of model, data, talent, and investment you’ll need.

For instance, if you want a neural network that can label the files in your image archive, there are plenty of pre-trained convolutional neural networks (e.g., ResNet, Inception) and public datasets (e.g., ImageNet and Microsoft COCO) that you can use out of the box. You can set up the deep learning model on your own server and run your images through it. Alternatively, you can sign up for an API-based service such as Amazon Rekognition or Microsoft Azure Computer Vision. In this case, inference will be done in the service provider’s servers.

But suppose you’re working for a large agriculture company and want to develop an image classifier that runs on drones and can detect weed in crops. Hopefully, the technology will help your company switch to precision application of herbicide to cut down costs, waste, and the negative effects of chemicals. In this case, you’ll need a more specialized approach. You’ll have to consider constraints on the machine learning model and the data. You need a neural network that is light enough to run on the compute resources of edge devices.